Fault diagnosis model of dual fluidized bed gasification device based on LSTM and voting decision

YANG Xin; GAO Xudong; LI Yancang; CHEN Hongwei; LIU Yuqiang; GAO Yanfeng

doi:10.19666/j.rlfd.201908190

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Fault diagnosis model of dual fluidized bed gasification device based on LSTM and voting decision

更新时间：2025-09-28

- Fault diagnosis model of dual fluidized bed gasification device based on LSTM and voting decision
- Thermal Power Generation Vol. 49, Issue 2, Pages: 52-57(2020)
- 作者机构：
  
  1. 河北工程大学水利水电学院
  2. 华北电力大学设备状态监测与控制教育部重点试验室
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201908190
  CLC： TK229
- Published Online：15 January 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
YANG Xin, GAO Xudong, LI Yancang, et al. Fault diagnosis model of dual fluidized bed gasification device based on LSTM and voting decision[J]. Thermal power generation, 2020, 49(2): 52-57.
DOI：

YANG Xin, GAO Xudong, LI Yancang, et al. Fault diagnosis model of dual fluidized bed gasification device based on LSTM and voting decision[J]. Thermal power generation, 2020, 49(2): 52-57. DOI： 10.19666/j.rlfd.201908190.

摘要

针对双流化床系统用于生物质气化时出现的结块和堵塞问题

本文提出一种基于长短期记忆神经网络(LSTM)和软投票(soft voting)决策的故障诊断模型。首先

在双流化床冷态试验系统上模拟结块和堵塞故障

采集提升管和气化室对应的压力波动信号;然后将采集的原始信号分解为合适长度的小样本

并作为LSTM的输入进行模型训练和测试;最后采用soft voting方式对测试样本的输出进行决策

确定故障类别。将本文提出的模型与多个常见模型进行仿真模拟

结果表明本文模型故障诊断准确率(91.25%)高

能准确预测系统出现的故障

具有一定的实际应用价值。

Abstract

Aiming at solving the agglomeration and blockage problem occurred during biomass gasification in dual fluidized bed system

a fault diagnosis model based on long short-term memory network(LSTM) and soft voting is proposed. Firstly

the agglomeration and clogging faults are simulated on the dual fluidized bed cold state experimental system

and the pressure fluctuation signals corresponding to the riser and the gasification chamber are collected. Secondly

the original single is decomposed into small samples with suitable length

and used as the input of the LSTM model for training and test. Finally

the soft voting method is used to make decisions on the output of the test samples to determine the fault category. Compared with many common models

the experimental results show that the proposed model has higher fault diagnosis accuracy(91.25%)

which has certain practical application value.

关键词

Keywords

references

Views

377

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

MULTISENSOR INFORMATION MERGING TECHNOLOGY AND IT'S APPLICATION IN VIBRITION FAULT DIAGNOSIS OF ROTARY MACHINES

APPLICATION OF EXTENDED Petri NETWORK INTO FAULT DIAGNOSIS OF THERMAL POWER UNITS

APPLICATION OF MULTI-DIMENSIONAL BP NEURAL NETWORK IN FAULT DIAGNOSIS OF BOILERS

RESIDUAL SPACE OF KERNEL PRINCIPAL COMPONENT ANALYSIS BASED SENSOR FAULT DIAGNOSIS

Kernel principal component analysis based fault diagnosis for condenser systems

Related Author

JIN Fenghua

LI Luping

LUO Yi

CHEN Yu-fei

MA Zeng-huiNorth China Electric Power University

GAO Jianqiang

CHEN Peng College of Energy

ZHOU Weiqing1

Related Institution

Changsha University of Science and Technology

North China Electric Power University

College of Energy, Power and Mechanical Engineering, North China Electric Power University

Huaneng International Power Co., Ltd

AI问答

⁰